Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/50—Depth or shape recovery
  • G06T7/55—Depth or shape recovery from multiple images
  • G06T7/593—Depth or shape recovery from multiple images from stereo images
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
  • H04N13/20—Image signal generators
  • H04N13/204—Image signal generators using stereoscopic image cameras
  • H04N13/239—Image signal generators using stereoscopic image cameras using two 2D image sensors having a relative position equal to or related to the interocular distance
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
  • G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
  • G01N23/04—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
  • G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
  • G01N23/06—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and measuring the absorption
  • G01N23/083—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and measuring the absorption the radiation being X-rays
  • G01N23/087—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and measuring the absorption the radiation being X-rays using polyenergetic X-rays
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
  • G01V5/00—Prospecting or detecting by the use of ionising radiation, e.g. of natural or induced radioactivity
  • G01V5/20—Detecting prohibited goods, e.g. weapons, explosives, hazardous substances, contraband or smuggled objects
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/0002—Inspection of images, e.g. flaw detection
  • G06T7/0004—Industrial image inspection
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
  • H04N13/20—Image signal generators
  • H04N13/204—Image signal generators using stereoscopic image cameras
  • H04N13/254—Image signal generators using stereoscopic image cameras in combination with electromagnetic radiation sources for illuminating objects
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/10—Image acquisition modality
  • G06T2207/10116—X-ray image
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/30—Subject of image; Context of image processing
  • G06T2207/30108—Industrial image inspection

Definitions

• the present invention relates to the field of radiation imaging technology, and more particularly to a method for material identification using dual-view multiple-energy transmission images. Background technique
• Radiation imaging technology enables the transmission of the object's transmission image in a non-contact state by means of the high-energy X-ray penetration capability.
• the working principle of scanning radiation imaging uses X-rays emitted by the radiation source, and the X-rays are received by the detector after being received by the detector, and then converted into an electrical signal input image acquisition system, by image The acquisition system inputs an image signal into a computer display to display the detected image.
• the transmission image formed by radiation imaging is a projection of all objects penetrated by the X-ray beam stream, and does not include information in the depth direction.
• the scanned image is an image formed by superimposing all objects along the direction of the scanning beam, which is not conducive to checking the items hidden behind other objects, and also cannot identify the material of the transmitted image.
• a more mature main body reconstruction technology uses computed tomography, but the disadvantages of this technology are: not only complicated equipment, high cost, but also unable to quickly check large objects. The pass rate is low, and again, the material of the transmissive object cannot be identified.
• the dual-view radiation transmission image processing technique is a radiation imaging technique that can detach an object located at a different depth of the slice in the detection space from the image, thereby removing the occlusion. This technique is used to strip certain overlapping objects in the transmitted image so that the obscured object is more visible in the image, but the material properties cannot be identified.
• the multi-energy transmission image recognition technology is a radiation transmission image processing technique that identifies properties of materials such as organic substances, mixtures, metals, etc., by utilizing the characteristics that different materials have different ray attenuation capabilities for different energies.
• this technique can only identify the properties of the object that accounts for the main part of the attenuation. If the object to be identified absorbs only a small fraction of the total attenuation dose, it is not possible to use this technique to identify the properties of such an object.
• the present invention provides a scanning radiation identification-imaging method for a large object radiation inspection system having a simple structure.
• the method is a material identification method for combining a dual-view technique and a multi-energy transmission image technique for performing a transmission image.
• the method firstly detects the space by the dual-view technique.
• the tomographic template of the intermediate object along the depth direction is then reconstructed from the transmission image, and finally the multi-energy technique is used to identify the object with successful gray reconstruction in the fault.
• a method for material identification using a dual-view multi-energy transmission image includes the steps of: passing two X-rays at a certain angle through an object to be inspected, thereby obtaining left and right transmission image data, and transmitting the left and right directions The image is segmented, and the results of the segmentation are matched;
• Material identification is performed on objects with successful grayscale reconstruction in the fault.
• the invention provides a method for material identification by using dual-view multi-energy transmission images, by which an object covering the main component of the ray absorption can be removed for an object overlapping in the direction of the beam, so that the original Objects that are not apparent due to the secondary component of the ray absorption become apparent and can be identified by their material properties: such as organic matter, mixtures, metals, etc.).
• the method according to the invention makes it possible to identify the non-primary components in the direction of the radiation, which lays the foundation for the automatic identification of explosives, drugs and other harmful objects contained in the container.
• FIG. 1 shows a schematic diagram of detecting a spatial fault according to the dual view technique of the present invention
• FIG. 2 shows a specific flow chart of image segmentation in accordance with the present invention
• Figure 3 shows an example of a template map of a matched fault according to the present invention.
• Figure 5 shows an example of a left view obtained by the method according to the invention and its greyscale reconstruction
• Figure 6 shows a flow of grayscale grayscale reconstruction including different energies in accordance with the present invention
• Figure 7 shows a detailed flow of grayscale reconstruction for a single energy in accordance with the present invention.
• Figure 8 is a flow chart showing the material identification of an object in any layer according to the present invention.
• Figure 9 is a multi-energy recognition effect diagram for a plurality of materials without overlap;
• Figure 10 shows a non-peeling recognition effect diagram
• Figure 11 is a diagram showing the multi-energy material recognition effect of the double-view occlusion peeling process.
• Figure 12 is a block diagram of a dual-view multi-energy scanning radiation imaging system in accordance with the present invention.
• FIGS. 13A and 13B are top views of a schematic layout of a dual-view multi-energy scanning radiation imaging system in accordance with the present invention.
• Figure 14 is a side elevational view showing the arrangement of a dual angle scanning multi-energy radiation imaging system in accordance with the present invention. detailed description
• a specific implementation of the method of dual-view multi-energy transmission image material identification according to the present invention is described in three parts hereinafter. I. Using a dual-view processing technique for each energy double-view image to obtain a tomogram template corresponding to the transmission image of the energy, and combining the fault templates of different energies into a template of a set of tomographic images.
• FIG. 1 is a schematic illustration of the detection of spatial faults in accordance with the dual viewing angle technique of the present invention.
• the detection space is a three-dimensional space formed by a region scanned by a sector of a radiation source (coordinate origin 0) to a vertical array of detectors and ?: where 0 ⁇ Coordinate origin; S: source position; left detector array; p: right detector array; 0L left beam; OR: right beam; ⁇ : left and right beam angle.
• the scan direction (vertical upward) is the positive direction of the f-axis
• the coordinate value is the scan ordinal number
• the direction of the detector arrangement (perpendicular to the outside of the paper) is the positive axis
• the coordinate value is the detector ordinal number to the horizontal To the right is the Z-axis positive direction
• the coordinate value is the fault ordinal number.
• the space rectangular coordinate system is established with the position 0 of the ray source as the coordinate origin, and we call the space where the angular coordinate system is located as the detection space.
• the fault is a series of spatial planes parallel to the X- 0-y plane, in the figure
• the dotted line shows the projection of the plane of the fault on the plane, and the depth of each layer is the distance between the plane of the layer and the plane.
• A is the projection of the main beam direction of the left beam on the - ⁇ z plane, /? is right
• 0 is the angle formed by the projection of the left and right beams on the - plane.
• a template map of the detected space object can be obtained by the image edge extraction technique. That is, several edges are obtained by detecting local discontinuities, and then they are connected.
• the edge extraction method is reliable in X-ray transmission image segmentation due to the inherent characteristics of the X-ray transmission image when the objects overlap.
• the present invention simultaneously uses Sobel and Canny edge detection operators to extract edges, and then combines the edges detected by the two detection operators.
• the edge obtained by the edge is connected to form a closed area, thereby realizing the division of the left and right views.
• Figure 2 shows a detailed flow chart of image segmentation in accordance with the present invention.
• edges are extracted.
• Sobel and Canny edge detection operators are simultaneously used to extract edges.
• ⁇ represents the pixel gray value of the i-th column and the j-th row in the image
• ⁇ ⁇ ' ⁇ is a set of all pixel points of the image
• the present invention uses the Sobel edge detection operator for the number
• Each pixel of the image [inspects the weighted difference of the gray levels of the upper, lower, left and right neighbors, the neighboring point weight is significant, and the next nearest neighboring point weight is small. Its definition is as follows:
• , ⁇ ⁇ ⁇ 1 are the convolution operator
• ⁇ is the convolution of the i-th column and the j-th row
• the matrix definition of the convolution operator is Then select the threshold 73 ⁇ 4, and the point ( ) that meets the condition s(/, )>73 ⁇ 4 is the step edge point, which is the edge image.
• the general steps of the Canny edge detection algorithm are: first smoothing the image with a Gaussian filter; then using the finite difference of the first-order partial derivative to calculate the amplitude and direction of the gradient; non-maximum suppression of the gradient amplitude; and finally using the double threshold
• the algorithm detects and joins edges.
• the Canny operator uses a double threshold algorithm to reduce false edges. That is, the non-maximum suppression image is binarized with two thresholds A and 7, ⁇ 2 ⁇ , to obtain two threshold edge images N, and N 2 (i,j)o N 2 (, ) is obtained using a high threshold. Contains few false edges, but with discontinuities. Then connect the edges into outlines in N 2 ( , _ ).
• the algorithm finds the edges that can be joined at the endpoints of the edges in 7 '), and the algorithm continuously finds the edges in the corresponding 8 neighborhoods, so that the algorithm continuously collects the edges until ⁇ ('',') is connected to form the contour. .
• a closed edge is obtained.
• the edges detected by the Sobel and Canny edge detection operators are combined and edge-joined to close them.
• the preliminary edge map of the present invention is the result of finding a logical "or" for the binary edge image obtained by the above two operators. Due to the influence of noise and the like, the edges obtained by the above methods are generally still discontinuous, so they need to be connected.
• the invention connects the edge pixels according to their continuity in the gradient amplitude or gradient direction. For example, if the pixel ( ⁇ t) is in the neighborhood of the pixel (x, and their gradient magnitude and gradient direction are satisfied at a given threshold:
• step 03 the segmentation of the view is performed according to the obtained closed edge. Since the closed edge divides the image into two areas, the morphological expansion-corrosion operation can be used to find points that belong to the interior of the area. Then, the region growth method can be used to fill the pixels in the region with the value "1", and the pixels outside the region are filled with the value "0", and the binary template of the inner region of each closed edge is obtained.
• the size of the map is equal to the projection of the detection space on the plane, ie the number of scans (width) of the number of X detectors (height). This completes the image segmentation and gets the object template.
• objects on the two template maps are matched according to a certain rule by a dual-view technique. That is, the connected area with each internal value of 1 in the left template picture is compared with each template in the right template picture one by one, and the corresponding template of the same object in the right view is found. Thus, each object that matches successfully corresponds to a template in the left and right views.
• the difference in position between the two templates in the horizontal direction is the parallax ⁇ ".
• ⁇ , and / ⁇ are the position of the center of gravity of the matching object in the tomographic template in the left and right views, and the parallax is proportional to the depth of each layer.
• Figure 3 is a diagram showing an example of a template map of a matched fault in accordance with the present invention. See Figure 3, which shows the left and right view segmentation results.
• Figure 3 (a) is a left view object template
• Figure 3 (b) is a right view object template, the template of which can be seen as a rectangle.
• the fault template of the transmission image obtained according to the dual-view tomography technique reflects the front-back position of the object in the depth direction in the detected space, and the geometry of the fault template reflects the shape contour of the object.
• FIG. 4 is a flow chart showing a template diagram of each energy layer obtained in accordance with the present invention.
• a set of transmission images containing the respective radiant energy is first established.
• a double loop operation with nesting inside and outside is performed, and the inner and outer dashed boxes respectively represent the inner and outer double loops.
• the inner loop is a template tomogram generation loop.
• the system establishes a matching object set to match the object number as a loop variable, according to steps 01 to 03 described in (1), to create, match, and object the object in the dual-view transmission image of an energy. Parallax calculation.
• a template of parallax approximation is merged into the same tomogram in a logical or logical manner to obtain a template tomogram set of matched objects in each fault under a certain radiant energy.
• the outer loop is a loop for different radiant energies.
• an inner loop is generated, a tomogram containing a template matching the good objects at each fault depth, and a transmission image generated under the next energy are selected.
• the image repeats the above steps until the transmitted image of all energy is processed.
• the template tomogram sets of different energies are layered in a logical or logical manner and are identified by a fault depth. Each layer contains a set of template tomograms of several object templates. 2. According to the fault template, the process of gray reconstruction of multiple energies is performed separately.
• the fault template map obtained above only reflects the geometry of the object and its spatial position in the container. If material identification is to be performed, gray value reconstruction of multiple energies must also be performed. After gray value reconstruction, we can obtain the gray values of various energies of each segmented object. Thereby material identification of the object can be achieved.
• grayscale reconstruction for each energy, we reconstruct the objects in each fault based on the two-view grayscale reconstruction method that is stripped from the outside to the inside. That is: first, the gray scale of the matched object in the X- 0-y plane is directly reconstructed (directly adjacent to the background area), and the background is still the original background value, and the object is curved in the outline of the object. The reconstructed grayscale image of the gray value to be obtained is scanned, and the object is stripped from the original image using the reconstructed grayscale. Then do the same for the outer layer of the object, repeat the above procedure until all the matched objects have been reconstructed.
• Figure 5 (a) Taking the left view shown in Figure 5 (a) as an example, we perform grayscale reconstruction in combination with a template map similar to that shown in Figure 4.
• three objects overlap each other, from the outside to the inside: a larger rectangle, a smaller rectangle, and a smaller ellipse.
• Figures 5(b), 5(c) and 5(d) show the effect of grayscale reconstruction.
• Figure 5 (b) is the outermost side
• Figure 5 (c> is the middle
• Figure 5 (d) is the innermost side.
• Figure 5 (b) is the result of grayscale reconstruction of the outermost object, where the gray value of the light area is the background value in the original picture, and the gray value of the dark area is the gray area of the light area minus the outermost object reconstruction.
• Gray value, dark area wheel The contour is the same as the contour in the template image of the object, and is a larger rectangle
• Figure 5 (c) is the grayscale reconstruction result of the intermediate object, wherein the gray value of the light color region is the background value in the original image, and the dark region is The gray value is the gray area minus the intermediate object reconstructed gray value, and the dark area contour is the same as the contour of the object template, which is a smaller rectangle
• Figure 5 (d) is the innermost object Gray-scale reconstruction results, wherein the gray value of the light-colored area is the background value in the original image, and the gray value of the dark-colored area is the gray-scale area minus the gray-scale area, and the dark area contour and the The template of the object has the same outline and is an elli
• Figure 6 shows the flow of grayscale grayscale reconstruction including different energies.
• the first weight is a fault set, which contains all template tomograms with the fault depth as the element distinguishing mark; the second weight is the object set, and the object serial number is used to distinguish the mark, including Each matched object at a specific fault depth; the third weight is an energy set, with different radiant energy as a distinguishing mark, and a transmission gradation reconstructed map at a specific energy of a specific matched object.
• the above method of reconstructing the grayscale image of the object and completing the reconstruction of the grayscale image of an energy is to perform the grayscale reconstruction of the object by the method of peeling off from the outer to the inner and the grayscale.
• the objects have all been completed.
• the flow chart is shown in Figure 7.
• FIG. 7 shows a detailed flow chart of grayscale reconstruction in accordance with the present invention. Referring to Figure 7, a detailed implementation process of the grayscale reconstruction according to the present invention will be described in detail.
• the gray scale reconstruction of the object is performed by the method of sequentially peeling off from the outside to the inside and the gray scale.
• step 01 a set of grayscale reconstruction candidate objects is created using the object obtained by the image segmentation process; in step 02, an attribute of an object is obtained.
• step 03 it is confirmed whether the acquired object has an edge adjacent to the background.
• step 04 the acquired object has a grayscale reconstructed object if it has an edge adjacent to the background; if the object has an object occlusion, the grayscale of the occlusion is reconstructed.
• the object is erased in the image.
• the gray scale of the reconstructed object be equal to the outer gray level of the edge minus the gray level of the edge side, that is,
• gray values of various energies of the object represented by each template in each layer can be obtained. These gray values differ depending on the energy. By analyzing these differences, the material in any layer can be identified.
• FIG. 8 shows the flow of the above identification process.
• a "fault-object-energy" triple set obtained by the second part of the implementation process is introduced.
• a double cycle of "fault-object” is then carried out.
• the outer dotted line frame is a cycle for each fault depth.
• the present invention creates a "object-energy" double set containing the matched objects of the fault and containing the grayscale reconstruction map for each energy of each object.
• the object loop in the inner dashed box is performed, that is, the loop in the object with the object number is marked for all the objects in the matching graph in a certain fault.
• Painted into a color tomogram in such a color tomogram: the identified object
• the outline of the body is determined by the outline of the template, and the color of the fill in the outline is determined by the combination of the gray scale reconstruction result and the material recognition result. Determination of Color
• the present invention will be described below in the related description of FIG. Then, create a new "object-energy" set for the next fault, and perform material identification and color map reconstruction until all faults have been processed the same.
• all the color material recognition effect tomograms obtained above are integrated into a set of color material recognition effect tomograms of objects on different faults, which is the final result obtained by the dual-view and multi-energy image processing technology of the present invention. .
• the multi-energy material identification method can identify the difference of the gray levels of different energy transmission maps for objects without overlap.
• Orange is the identification color of organic or light materials
• green is the identification color of light metals or mixtures
• blue is the metal identification color.
• the recognition effect diagram is as shown in Fig. 9.
• FIG. 9 there is shown a multi-energy recognition effect diagram of graphite, aluminum, iron, lead, and polyethylene rectangular at a mass thickness of 30 g.
• the left-to-right targets are arranged in the order of graphite, aluminum, iron, lead, and polyethylene.
• the recognition order is: orange, green, blue, blue, and orange. The material is identified correctly.
• Fig. 10 shows a non-peeling recognition effect diagram.
• FIG. 10 there is a large rectangular steel plate obstruction, in the middle is a blocked cylinder containing petroleum liquefied gas, the left is a carton-mounted disc, and the right is a carton containing cigarettes. It can be seen that the identification of the petroleum liquefied gas in the cylinder and the cigarette in the right side of the carton basically recognizes the error, and a partial recognition error occurs in the carton loaded on the left side of the carton. If the double-view layering method is used to peel off the obstruction and then recognize it, the effect is as shown in Fig. 11.
• Figure 11 for the dual-view occluded multi-energy material recognition effect diagram: where Figure 11 (a) is the occlusion; Figure 11 (b) is the identified object.
• Figure 11 shows that after using the dual-view occlusion stripping multi-energy material identification technology, the steel sheet covering in Figure 11 (a) is recognized as blue, which is metal, and the carton-mounted disc in Figure 11 (b) is liquefied. Both LPG and carton cigarettes are identified as orange, organic, and the recognition results are correct.
• Figure 12 is a block diagram of a dual viewing angle multi-energy scanning radiation imaging system in accordance with the present invention. As shown in Fig. 12, the dual-view multi-energy scanning radiation imaging system of the present invention includes the following devices:
• Multi-energy radiation source 1 X-ray generator, capable of generating X-ray beams of different energies.
• the beam current controller receives the X-rays emitted by the radiation source 1 and emits two symmetrical or asymmetrical X-rays at an angle.
• the left detector array 4 receives X-rays of different energies emitted by the multi-energy radiation source and converts them into electrical signals for input to the left image acquisition system 6.
• the right detector array 5 receives X-rays of different energies emitted by the multi-energy radiation source and converts them into electrical signals for input to the right image acquisition system 7.
• the left image acquisition system 6 receives an electrical signal from the left detector array 4 and acquires left image data therefrom.
• the right image acquisition system 7 receives the electrical signal from the right detector array 5 and obtains the right image data therefrom.
• the computer processing system 8 receives the left and right image data from the left image acquisition system 6 and the right image acquisition system 7 and processes them, respectively displaying the image of the measured object in the computer display, and also displaying the different depths reconstructed by the two. Tomographic image of the fault.
• the radiation source 1 emits two symmetrical or asymmetrical X-rays at an angle through the beam controller 2, and each X-ray beam passes through the object to be inspected 3 by the left detector array 4 and
• the right detector array 5 receives; then converts the electrical signals into the left image acquisition system 6 and the right image acquisition system 7, respectively, and the image data in the left and right image acquisition systems 6 and 7 can be processed by the computer processing system 8 respectively.
• the image of the measured object is displayed on the computer display, and the tomographic image of the depth at different depths is also displayed.
• the dual-view multi-energy scanning radiation imaging system can respectively obtain a tomographic template map of a transmission image corresponding to the energy of the double-view image of each energy by using a dual-view processing technique, and merge the tomographic templates of different energies.
• a template of a set of tomographic images according to the tomographic template, respectively, a gray reconstruction process of multiple energies is performed, and the reconstructed tomographic image is subjected to material identification of any layer.
• DETAILED DESCRIPTION OF THE INVENTION The description of the dual-view multi-energy scanning radiation imaging method of the present invention is not repeated here.
• a preferred embodiment of the invention utilizes a double-slit collimator as a beam current controller for beam current control of the radiation emitted by the source.
• FIG. 13 and 14 are a plan view and a side view, respectively, showing a schematic view of the arrangement of the apparatus embodying the present invention.
• 13A is a case where the beam ray is symmetrical
• FIG. 13B is a case where the beam ray is asymmetrical.
• the beam controller is provided with two collimating slits to form a symmetric or asymmetric X-ray of the radiation source with a certain angle of the beam, left and right detector arrays.
• 4 and 5 respectively scan the object to be measured at a symmetrical angle to the beam fan defined by the collimating slit of the double-slit collimator, and transmit the respective electrical signals to the corresponding left and right images.
• the system then performs image processing by computer processing system 8 to derive a tomographic image containing depth information.
• this method can not only enter the main components of the transmitted image in the ray direction.
• the traditional multi-energy material identification method can only identify the main components in the ray direction. For example, in the direction of the ray, a thicker steel plate overlaps with a bag of smaller drugs. If the traditional multi-energy material identification method is used, in the ray direction, only the steel plate can be recognized, and the drug cannot be identified. .
• the material identification can be performed separately in each fault, and not only can It is also recognized that steel maple (the main component in the direction of the ray attenuates the ray is large), and it is also possible to identify the drug (the non-primary component in the ray direction is less attenuated by the ray).
• This method is particularly useful for material identification of container transmission images. For the transmission scanning image of the container, because the container has a large thickness and a long radiation penetration distance, harmful objects such as explosives and drugs are often non-main components in the ray direction. Therefore, the method lays a foundation for automatically identifying harmful substances such as explosives and drugs in a container transmission scanned image.

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